Reference : Example-Dependent Cost-Sensitive Logistic Regression for Credit Scoring
Scientific congresses, symposiums and conference proceedings : Paper published in a book
Engineering, computing & technology : Computer science
http://hdl.handle.net/10993/18656
Example-Dependent Cost-Sensitive Logistic Regression for Credit Scoring
English
Correa Bahnsen, Alejandro mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
Aouada, Djamila mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
Ottersten, Björn mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
3-Dec-2014
2014 13th International Conference on Machine Learning and Applications
IEEE
263-269
Yes
International
9781479974153
Detroit
Uni
International Conference on Machine Learning and Applications
from 03-12-2014 to 06-12-2014
IEEE
Detroit
United States
[en] Cost sensitive classification ; Credit Scoring ; Logistic Regression
[en] Several real-world classification problems are example-dependent cost-sensitive in nature, where the costs due to misclassification vary between examples. Credit scoring is a typical example of cost-sensitive classification. However, it is usually treated using methods that do not take into account
the real financial costs associated with the lending business. In this paper, we propose a new example-dependent cost matrix for credit scoring. Furthermore, we propose an algorithm that introduces the example-dependent costs into a logistic regression. Using two publicly available datasets, we compare our proposed method against state-of-the-art example-dependent cost-sensitive algorithms. The results highlight the importance of using real financial costs. Moreover, by using the proposed cost-sensitive logistic regression, significant improvements are made in the sense of higher savings.
University of Luxembourg: High Performance Computing - ULHPC
http://hdl.handle.net/10993/18656
10.1109/ICMLA.2014.48

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